With the social and technology developments, in recent years, mobile banks, mobile wallets, on-line shopping and the like e-commerce services are more and more widely promoted on smart mobile devices. Correspondingly, users are imposing higher and higher requirements on performance of integrated electronic elements and security of network information. Fingerprints, due to uniqueness and stability thereof, have become an effective means to identify user identities. There are a plurality of types of fingerprint sensors. Currently, three types of fingerprint sensors are prevailing: optical imaging fingerprint sensors, crystal capacitive (or pressure-sensitive) fingerprint sensors and ultrasonic imaging fingerprint sensors. An optical device acquires fingerprint images by using the full reflection principle and by using a CCD device, which achieves a better effect. However, the device is abrasion resistant, but the cost is high and the volume is large, which is thus unsuitable for mobile terminals having a high requirement on integration. An ultrasonic imaging directly scans dermal tissues, and thus dirt or oil accumulated on the skin surface cause less impact to acquisition of the image. However, the cost of the device is extremely high, and currently there is no matured product market. The crystal capacitive fingerprint sensor is manufactured according to standard CMOS technique, and acquires quality images (image quality achieved by improving the gain by using software). In addition, this fingerprint sensor has small size and low power consumption, and thus the cost thereof is much lower than that of the other sensors.
The crystal capacitive fingerprint sensor includes a plurality of detection units arranged in an array. When a finger touches the detection unit, the fingerprint unit is equivalent to an anode of a capacitor, the skin of the finger becomes a cathode of the capacitor, and the capacitance (or inductance) is different because a practical distance from the fingerprint of the finger to the detection unit is different due to different depths of the grain of the finger (that is, “ridges” and “valleys” of the finger). A fingerprint image formed by ridges and valleys of the finger may be detected according to this principle. FIG. 1 illustrates a commonly used fingerprint detection circuit in a fingerprint detection system. An input signal VIN_1 from a finger and a canceling signal VCAN from a high-precision signal source are simultaneously input to the detection unit; and in an output signal VPXL_1 of the detection unit, a dotted-line portion represents a useful signal amplitude of the fingerprint information, and a solid-line portion represents a base signal amplitude. This method may be defective in that, firstly, an amplitude of the VCAN signal output from the high-precision signal source fails to follow VIN_1 which may dynamically change (this is because VIN_1-VIN_n are determined by a coupling capacitance between the finger and the detection unit, and the coupling capacitance may dynamically change). Therefore, the signal amplitude of VPXL_1 may still include a portion of base signal amplitudes, and the final output signal VOUT_1 upon amplification by an amplifier is also the same. Secondly, assume that VIN_1 is fixed, to acquire an ideal base signal cancellation effect, high requirements are imposed on the indicators of the high-precision signal source, and thus implementation of the corresponding signal source inside the chip may cause higher cost and more power consumption.